Parth Bhawsar

Key Skills

Software

Other

Appen

Top Subject Matter

AI/ML Training Data

Search Engine/ML Model Evaluation

AI/ML Model Development

Top Data Types

Text

Image

Top Task Types

Classification

Data Collection

Freelancer Overview

Data Annotator. Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and Appen. Education includes Bachelor of Technology, Indian Institute of Information Technology, Vadodara (2022). AI-training focus includes data types such as Text and labeling workflows including Classification, Evaluation, and Rating.

Labeling Experience

AI Data Contributor

OtherTextData Collection

At Uber AI Solutions, I contributed to AI dataset annotation and labeling tasks to enhance machine learning models. My role required meticulous data handling and attention to consistency while adhering to project-specific annotation standards. I provided high-quality labeled data essential for the continuous improvement of AI systems. • Followed project guidelines for labeling accuracy. • Collaborated with teams handling similar datasets. • Submitted timely updates and quality checks for datasets. • Focused on delivering reliable and annotated data for model training.

At Uber AI Solutions, I contributed to AI dataset annotation and labeling tasks to enhance machine learning models. My role required meticulous data handling and attention to consistency while adhering to project-specific annotation standards. I provided high-quality labeled data essential for the continuous improvement of AI systems. • Followed project guidelines for labeling accuracy. • Collaborated with teams handling similar datasets. • Submitted timely updates and quality checks for datasets. • Focused on delivering reliable and annotated data for model training.

2025 - Present

Search Engine Evaluator

AppenText

As a Search Engine Evaluator with Appen (CrowdGen), I evaluated search results for relevance, accuracy, and user intent. My feedback contributed directly to improving machine learning ranking algorithms for better user experiences. I completed various real-world query evaluation tasks involving contextual quality assessments. • Assessed quality and intent of search results. • Provided detailed feedback on algorithmic suggestions. • Conducted regular reviews and submitted evaluation reports on schedule. • Maintained accuracy and consistency in output ratings.

As a Search Engine Evaluator with Appen (CrowdGen), I evaluated search results for relevance, accuracy, and user intent. My feedback contributed directly to improving machine learning ranking algorithms for better user experiences. I completed various real-world query evaluation tasks involving contextual quality assessments. • Assessed quality and intent of search results. • Provided detailed feedback on algorithmic suggestions. • Conducted regular reviews and submitted evaluation reports on schedule. • Maintained accuracy and consistency in output ratings.

2025 - Present

Data Annotator

OtherTextClassification

As a Data Annotator at Innodata (Multimango), I performed large-scale data annotation tasks to support AI model training using both text and image datasets. My responsibilities included following strict annotation guidelines and ensuring consistency across the dataset. I also conducted quality assurance checks to verify and enhance dataset reliability. • Maintained an annotation speed of 25–30+ cases per hour with high accuracy. • Executed validation processes and upheld high standards of data quality. • Updated and improved annotation protocols as required. • Frequently collaborated with team members to resolve guideline ambiguities.

As a Data Annotator at Innodata (Multimango), I performed large-scale data annotation tasks to support AI model training using both text and image datasets. My responsibilities included following strict annotation guidelines and ensuring consistency across the dataset. I also conducted quality assurance checks to verify and enhance dataset reliability. • Maintained an annotation speed of 25–30+ cases per hour with high accuracy. • Executed validation processes and upheld high standards of data quality. • Updated and improved annotation protocols as required. • Frequently collaborated with team members to resolve guideline ambiguities.

2025 - Present

Education

Work History